Scrutinize

scrutinize

I began to scrutinize the man closely

I caught up with a friend yesterday who is an up-and-coming in the Austin area and it turns out we are facing a lot of the same challenges in launching a new venture. We both are working to optimize processes in order to spend the most time possible on the core aspects of our firms. architect During a 2016 shareholders meeting, Elon Musk said “We’ve realized that the true problem, the true difficulty and where the greatest potential is, is building the machine that builds the machine. In other words, building the factory.” Similarly, Homebrew’s Hunter Walk has been quoted as saying “the company is the first product, and you have to be really intentional about how you build it before it’s ready to scale.” This highlights an important reality of building a great company or product: the processes and frameworks put into place will often decide the level of success achieved and those operational components should be designed with the end-goal in mind. , the best of the best almost always “perform the common uncommonly well.” As my partner Jonathan recently wrote The larger a company grows, the more difficult it becomes to put processes in place. This one is straightforward, as a begins to scale it becomes exponentially harder to move everyone in the same direction. Getting a group of 10 engineers to agree on a product development methodology is easier than asking 40 to do the same and infinitely easier than asking a team of 100 or more. This isn’t just an engineering or product problem, the same rules apply to marketing, finance, and sales. Bonus: it will make cross-functional execution that much easier. 1) Start Early: startup The best early-stage companies have an innate ability to focus when it is most difficult to do so. Early-on, startups are vulnerable to chasing shiny objects as everything feels like a big opportunity. Instead, avoid temptation and focus on executing toward one core product or market. Example tactics to accomplish this include: shorter meetings (i.e. standups), picking 2–3 metrics that are core to success (ex. contribution margin and conversion), and selecting 1–2 strategic priorities per timeframe. 2) Overly-Simplify: Be honest with yourself. One of the most effective practices I’ve seen teams use is the agile concept of a retrospective. This gives teams the chance to discuss what’s working, what isn’t and ideas for improvement. The most crucial element to this step is accountability, without it, these meetings don’t help move the company forward but instead become chances to be overly critical without inspiring action. Begin each “retro” with a review of how well the team executed on previous takeaways and keep score. 3) Scrutinize: Operations within early-stage companies often get overlooked as there are more exciting challenges to tackle. When processes are in place, no one notices because things are running smoothly and challenges do not begin to surface until later in the game. However, neglecting them is akin to puncturing a boat while it is still in port; it may leave the dock, but it won’t make the entire trip. Enjoy this post? Please share! Originally published at kevindstevens.com on September 1, 2017.

SOS: Start-Early, Overly-Simplify, Scrutinize

Discussions about Generative AI safety are more urgent than ever. As AI systems become more embedded in our daily lives, from personalized recommendations to complex decision-making processes, ensuring their safety and ethical alignment with human values is paramount. However, the complexity of AI systems requires a nuanced understanding of the multiple layers that contribute to their reliability and societal impact. To unravel this complexity, it helps to think of AI safety as an onion, with each layer addressing a critical facet of the AI lifecycle. Training Data (Quality and Fairness): At the core of any AI system lies the data on which it is trained. The adage "garbage in, garbage out" could not be more applicable here. AI models rely on vast amounts of data, and the quality of that data directly affects their performance. Poor-quality data introduces biases and inaccuracies, which can result in skewed outputs or even harmful outcomes. Ensuring the quality, diversity, and fairness of training data is a foundational step towards building systems that are not only accurate but also fair and representative of diverse groups. Algorithm (Tuning and Transparency): Moving outward, we reach the algorithm itself. Algorithms define how the AI interprets data, learns from it, and generates predictions. The choices made during algorithm tuning and development can have far-reaching consequences, and transparency in these processes is essential for accountability. By understanding how an AI model makes decisions, we can prevent the "black box" phenomenon, where even developers don’t fully understand how certain outputs are produced. Transparency enables stakeholders to scrutinize, audit, and improve AI systems continually, aligning them with intended ethical standards. Inference (Accuracy): As AI systems process data and make predictions, the accuracy of these inferences becomes the next critical layer. Inaccurate inferences can lead to costly errors, especially in high-stakes fields such as healthcare, finance, and law enforcement. Rigorous testing and validation of AI models are essential to ensure that they perform as expected in real-world scenarios. This layer underscores the importance of accuracy and reliability, setting a high standard for performance before deployment. Publication (Accountability): Accountability comes into play when AI models are deployed into society. Publication and documentation of AI systems' goals, methods, and expected outcomes create an essential checkpoint for their use. This layer of the onion ensures that the developers and operators of AI systems can be held accountable for their actions and decisions. Clear documentation, including the limitations and potential risks of the AI, helps stakeholders understand and anticipate its impact, fostering a culture of responsibility in AI deployment. Societal Impact (Human in the Loop): At the outermost layer lies the societal impact, where AI systems intersect with human lives. This is where "human in the loop" becomes essential. No AI system should operate in complete isolation from human oversight, especially when it comes to decisions affecting individuals and communities. Ensuring that humans have the ability to monitor, intervene, and correct AI processes can prevent unintended consequences, biases, or harmful actions. This final layer emphasizes the need for a balance between AI autonomy and human oversight, promoting AI systems that enhance human well-being rather than detract from it. Peeling back these layers reveals the complexity of AI safety, which cannot be addressed by focusing on any single aspect. Just like an onion, each layer builds upon the previous ones, contributing to a holistic approach to safety that spans from data quality to societal impact. As we continue to integrate AI into more areas of life, it's crucial to adopt this layered perspective. AI safety is not just a technical issue; it is a societal one that requires ongoing vigilance, transparency, and a commitment to ethical standards at every level. The future of AI depends on our ability to address each layer thoughtfully, creating systems that are as beneficial as they are innovative. By treating AI safety as an onion, we peel away the superficial and dig deep into the core of responsible AI development, creating a future where technology serves humanity rather than the other way around. For those interested, I wrote a more detailed discussion on this layered approach here: http://dx.doi.org/10.13140/RG.2.2.15152.57604 Happy for your comments and feedback. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here. Discussions about Generative AI safety are more urgent than ever. As AI systems become more embedded in our daily lives, from personalized recommendations to complex decision-making processes, ensuring their safety and ethical alignment with human values is paramount. However, the complexity of AI systems requires a nuanced understanding of the multiple layers that contribute to their reliability and societal impact. To unravel this complexity, it helps to think of AI safety as an onion, with each layer addressing a critical facet of the AI lifecycle. Training Data (Quality and Fairness): At the core of any AI system lies the data on which it is trained. The adage "garbage in, garbage out" could not be more applicable here. AI models rely on vast amounts of data, and the quality of that data directly affects their performance. Poor-quality data introduces biases and inaccuracies, which can result in skewed outputs or even harmful outcomes. Ensuring the quality, diversity, and fairness of training data is a foundational step towards building systems that are not only accurate but also fair and representative of diverse groups. Algorithm (Tuning and Transparency): Moving outward, we reach the algorithm itself. Algorithms define how the AI interprets data, learns from it, and generates predictions. The choices made during algorithm tuning and development can have far-reaching consequences, and transparency in these processes is essential for accountability. By understanding how an AI model makes decisions, we can prevent the "black box" phenomenon, where even developers don’t fully understand how certain outputs are produced. Transparency enables stakeholders to scrutinize, audit, and improve AI systems continually, aligning them with intended ethical standards. Inference (Accuracy): As AI systems process data and make predictions, the accuracy of these inferences becomes the next critical layer. Inaccurate inferences can lead to costly errors, especially in high-stakes fields such as healthcare, finance, and law enforcement. Rigorous testing and validation of AI models are essential to ensure that they perform as expected in real-world scenarios. This layer underscores the importance of accuracy and reliability, setting a high standard for performance before deployment. Publication (Accountability): Accountability comes into play when AI models are deployed into society. Publication and documentation of AI systems' goals, methods, and expected outcomes create an essential checkpoint for their use. This layer of the onion ensures that the developers and operators of AI systems can be held accountable for their actions and decisions. Clear documentation, including the limitations and potential risks of the AI, helps stakeholders understand and anticipate its impact, fostering a culture of responsibility in AI deployment. Societal Impact (Human in the Loop): At the outermost layer lies the societal impact, where AI systems intersect with human lives. This is where "human in the loop" becomes essential. No AI system should operate in complete isolation from human oversight, especially when it comes to decisions affecting individuals and communities. Ensuring that humans have the ability to monitor, intervene, and correct AI processes can prevent unintended consequences, biases, or harmful actions. This final layer emphasizes the need for a balance between AI autonomy and human oversight, promoting AI systems that enhance human well-being rather than detract from it. Peeling back these layers reveals the complexity of AI safety, which cannot be addressed by focusing on any single aspect. Just like an onion, each layer builds upon the previous ones, contributing to a holistic approach to safety that spans from data quality to societal impact. As we continue to integrate AI into more areas of life, it's crucial to adopt this layered perspective. AI safety is not just a technical issue; it is a societal one that requires ongoing vigilance, transparency, and a commitment to ethical standards at every level. The future of AI depends on our ability to address each layer thoughtfully, creating systems that are as beneficial as they are innovative. By treating AI safety as an onion, we peel away the superficial and dig deep into the core of responsible AI development, creating a future where technology serves humanity rather than the other way around. For those interested, I wrote a more detailed discussion on this layered approach here: http://dx.doi.org/10.13140/RG.2.2.15152.57604 http://dx.doi.org/10.13140/RG.2.2.15152.57604 Happy for your comments and feedback. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here . here

Peeling the Onion on AI Safety

Interaction, Repetition and Trust: Are These Opt-out?

The Best User Agent for Web Scraping

Най-добрият потребителски агент за уеб скрапинг

Den bedste brugeragent til web-skrabning

El mejor agente de usuario para el web scraping

O mellor axente de usuario para o rascado web

Ajan itilizatè a pi bon pou grate entènèt

סוכן המשתמש הטוב ביותר לגרידת אינטרנט

Webスクレイピングに最適なユーザーエージェント

Cel mai bun agent de utilizator pentru Web Scraping

Web Scraping සඳහා හොඳම පරිශීලක නියෝජිතයා

The Best User Agent yeWeb Scraping

Wakala Bora wa Mtumiaji wa Kuchakachua Wavuti

I-ejenti Yomsebenzisi Engcono Kakhulu Ye-Web Scraping

TL;DR: The evolution of trust, reputation and authority Trust No(One) Reputation has been the center of tribal human interaction, growing from word-of-mouth trust in small communities to algorithmically quantified scores in today's digital world. It's an invisible currency, shaping our social and professional lives and growing with the technology eating the world. The concept of reputation is not new in science fiction, but today trends with artificial intelligence, and the blockchain-based decentralized world we're currently building. One of the best thinkers in science fiction history: Isaac Asimov anticipated the importance of reputation in a hyper-connected future. His Foundation books revolve around predicting human behavior on a large scale with the idea of reputation management in governance and technology. Asimov's famous "psychohistory" relies on understanding and predicting the actions of huge groups of people based on their collective behaviors, mimicking the way reputational scores in digital systems work today. In software development, "fail-safe" systems with multiple redundancies rely heavily on trust. If a component fails, its partners bully it, lowering its votes on choosing a solution to a problem. Systems that coordinate to ensure reliability, like distributed databases or fault-tolerant microservices, use this voting concept. When a piece fails, another picks up the piece in the slack, keeping the system's reputation intact. The same happens with human trust networks, where the reliability of individuals or systems can buffer potential failures by relying on others to reinforce the gap. No chain is stronger than its weaker link. In the Black Mirror episode "Nosedive," reputation becomes an all-encompassing social credit score, dictating every aspect of a person’s life, from their social interactions to access to essential services. The more highly-rated someone is, the more power and privilege they are afforded. This TV chapter isn’t far from reality in some parts of the world today, where similar systems track and quantify people's reputations for various societal privileges. It is also a warning tale that explores the dystopian side of what happens when reputation becomes too pervasive and omnipresent. Another historical system built around reputation is the ELO chess rating. This system assigns players a numerical rating based on their performance, and updates based on wins or losses. It mirrors how reputation systems are built today, where success and failure adjust your standing, whether in chess, on social media, or within a decentralized network like Web3. The decentralized web may make reputational systems more granular, with users earning trust in different networks for specific skills, trades, or behaviors. Losing trust in one domain might not affect others, which adds resilience and complexity to managing personal reputation. Reputation in the Decentralized World The Web3 revolution, powered by the blockchain, decentralizes everything from finance to ownership and reputation systems. In this world, reputation doesn’t rely on a single governing authority, like a government or corporation. It’s shared and validated across multiple nodes and distributed through secure protocols. You gain or lose reputation based on interactions across various decentralized networks, from participation in decentralized finance to your contributions to open-source projects. Reputation is key in decentralized autonomous organizations where your position in the community impacts how much influence you hold. It's a world where your contributions, not your credentials, define you. Smart contracts may also build mechanisms for handling disputes or disagreements in reputation, offering transparency and requiring users to trust the underlying code and the people who built and deployed it. You can lose your reputation by failing to deliver on promises or being caught in malicious activities, as your on-chain actions are visible for everyone to scrutinize. Social Media and the Current State of Reputation Today’s social media platforms run on a form of reputational economy, quantified through likes, retweets, and followers. Except the system is broken. Social networks are opaque, easily manipulated, and often, lack the nuance needed to reflect true reputational value. A person can build a massive following based on charisma or polarizing opinions, with little regard for trustworthiness or expertise. A single misstep can lead you to be "canceled," a form of reputational collapse that occurs when public sentiment turns against you, often without due process or a chance for redemption. The problem with today’s reputation on platforms like Twitter or Instagram is that it’s too centralized and shallow. Algorithms designed to maximize engagement and screen time, not accuracy or trust, determine what rises to the top. Cancel culture amplifies this flaw, where reputational loss can be swift and brutal, often driven by mob mentality rather than any objective measure of wrongdoing. Trust in the Scientific Model and Experts Trust plays an essential role in reputation. In science and technology, people often need to trust experts because testing everything individually is impossible. You can't build your own Large Hadron Collider to verify the Higgs’ Boson in your backyard. You also can’t recreate experiments in gene editing unless you are a professional in the area. You trust the experts, institutions, and peer-reviewed systems with authority set up over centuries. The rise of misinformation is showing us that trust is fragile. The scientific model works since it’s based on evidence, reproducibility, and rigorous debate. In an age where everyone has a platform and misinformation spreads like wildfire, the expert reputation can be undermined with a few viral tweets or videos, and some people feel smarter preaching the earth is flat without trusting tons of evidence against it. The future challenge, especially in decentralized systems, is to build trust without relying on traditional gatekeepers. Systems like Web3 offer new ways to prove expertise—such as cryptographic proof of contributions—but they also open the door to new forms of manipulation and deceit. Keeping your reputation in such a world requires personal integrity and robust systems of validation that the community trusts. The evolution of trust, from the gold standard to trusting institutions without physical assets, reflects a profound shift in how societies and economies operate. The Future of Reputation and Humanity Reputation might develop into something even more complex, dynamic, and dispersed in the future. In a decentralized world, reputation systems might be attached to digital identities that span multiple domains—from work and social interaction to financial activities. Imagine a world where you build your reputation by pilling up micro-reputations across different blockchains and platforms, all visible and all contributing to your overall standing in society. Artificial intelligence may bring even greater difficulties for humans in maintaining its reputation. A.I. could automate reputation management, predicting reputational damage or gains based on real-time data analysis. But as with any algorithmic system, there’s the risk of reinforcing biases and unjust penalties. Quantum computing could enhance cryptographic protocols to secure reputational data, but it could also lead to new forms of hacking and identity manipulation. Humanity and the mechanisms that measure reputation and trust will both keep changing. The key will be finding a balance between transparency and privacy, and ensuring that reputational systems are fair, decentralized, and resilient enough to resist manipulation. Reputation is the new currency. Like any form of currency, its value will depend on the systems and frameworks you build around it, the trust we place in those systems, and the collective understanding of what it means to be trustworthy. TL;DR: The evolution of trust, reputation and authority TL;DR: The evolution of trust, reputation and authority Trust No(One) Reputation has been the center of tribal human interaction, growing from word-of-mouth trust in small communities to algorithmically quantified scores in today's digital world. It's an invisible currency, shaping our social and professional lives and growing with the technology eating the world. The concept of reputation is not new in science fiction, but today trends with artificial intelligence, and the blockchain-based decentralized world we're currently building. One of the best thinkers in science fiction history: Isaac Asimov anticipated the importance of reputation in a hyper-connected future. His Foundation books revolve around predicting human behavior on a large scale with the idea of reputation management in governance and technology. Foundation Asimov's famous "psychohistory" relies on understanding and predicting the actions of huge groups of people based on their collective behaviors, mimicking the way reputational scores in digital systems work today. In software development, "fail-safe" systems with multiple redundancies rely heavily on trust. If a component fails, its partners bully it, lowering its votes on choosing a solution to a problem. Systems that coordinate to ensure reliability, like distributed databases or fault-tolerant microservices, use this voting concept. When a piece fails, another picks up the piece in the slack, keeping the system's reputation intact. The same happens with human trust networks, where the reliability of individuals or systems can buffer potential failures by relying on others to reinforce the gap. No chain is stronger than its weaker link. In the Black Mirror episode "Nosedive," reputation becomes an all-encompassing social credit score, dictating every aspect of a person’s life, from their social interactions to access to essential services. Black Mirror The more highly-rated someone is, the more power and privilege they are afforded. This TV chapter isn’t far from reality in some parts of the world today, where similar systems track and quantify people's reputations for various societal privileges. It is also a warning tale that explores the dystopian side of what happens when reputation becomes too pervasive and omnipresent. Another historical system built around reputation is the ELO chess rating. This system assigns players a numerical rating based on their performance, and updates based on wins or losses. It mirrors how reputation systems are built today, where success and failure adjust your standing, whether in chess, on social media, or within a decentralized network like Web3. The decentralized web may make reputational systems more granular, with users earning trust in different networks for specific skills, trades, or behaviors. Losing trust in one domain might not affect others, which adds resilience and complexity to managing personal reputation. Reputation in the Decentralized World The Web3 revolution, powered by the blockchain, decentralizes everything from finance to ownership and reputation systems. In this world, reputation doesn’t rely on a single governing authority, like a government or corporation. It’s shared and validated across multiple nodes and distributed through secure protocols. You gain or lose reputation based on interactions across various decentralized networks, from participation in decentralized finance to your contributions to open-source projects. Reputation is key in decentralized autonomous organizations where your position in the community impacts how much influence you hold. It's a world where your contributions, not your credentials, define you. Smart contracts may also build mechanisms for handling disputes or disagreements in reputation, offering transparency and requiring users to trust the underlying code and the people who built and deployed it. You can lose your reputation by failing to deliver on promises or being caught in malicious activities, as your on-chain actions are visible for everyone to scrutinize. Social Media and the Current State of Reputation Today’s social media platforms run on a form of reputational economy, quantified through likes, retweets, and followers. Except the system is broken. Social networks are opaque, easily manipulated, and often, lack the nuance needed to reflect true reputational value. A person can build a massive following based on charisma or polarizing opinions, with little regard for trustworthiness or expertise. A single misstep can lead you to be "canceled," a form of reputational collapse that occurs when public sentiment turns against you, often without due process or a chance for redemption. The problem with today’s reputation on platforms like Twitter or Instagram is that it’s too centralized and shallow. Algorithms designed to maximize engagement and screen time, not accuracy or trust, determine what rises to the top. Cancel culture amplifies this flaw, where reputational loss can be swift and brutal, often driven by mob mentality rather than any objective measure of wrongdoing. Trust in the Scientific Model and Experts Trust plays an essential role in reputation. In science and technology, people often need to trust experts because testing everything individually is impossible. You can't build your own Large Hadron Collider to verify the Higgs’ Boson in your backyard. You also can’t recreate experiments in gene editing unless you are a professional in the area. You trust the experts, institutions, and peer-reviewed systems with authority set up over centuries. The rise of misinformation is showing us that trust is fragile. The scientific model works since it’s based on evidence, reproducibility, and rigorous debate. In an age where everyone has a platform and misinformation spreads like wildfire, the expert reputation can be undermined with a few viral tweets or videos, and some people feel smarter preaching the earth is flat without trusting tons of evidence against it. The future challenge, especially in decentralized systems, is to build trust without relying on traditional gatekeepers. Systems like Web3 offer new ways to prove expertise—such as cryptographic proof of contributions—but they also open the door to new forms of manipulation and deceit. Keeping your reputation in such a world requires personal integrity and robust systems of validation that the community trusts. The evolution of trust, from the gold standard to trusting institutions without physical assets, reflects a profound shift in how societies and economies operate. The Future of Reputation and Humanity Reputation might develop into something even more complex, dynamic, and dispersed in the future. In a decentralized world, reputation systems might be attached to digital identities that span multiple domains—from work and social interaction to financial activities. Imagine a world where you build your reputation by pilling up micro-reputations across different blockchains and platforms, all visible and all contributing to your overall standing in society. Artificial intelligence may bring even greater difficulties for humans in maintaining its reputation. A.I. could automate reputation management, predicting reputational damage or gains based on real-time data analysis. But as with any algorithmic system, there’s the risk of reinforcing biases and unjust penalties. Quantum computing could enhance cryptographic protocols to secure reputational data, but it could also lead to new forms of hacking and identity manipulation. Humanity and the mechanisms that measure reputation and trust will both keep changing. The key will be finding a balance between transparency and privacy, and ensuring that reputational systems are fair, decentralized, and resilient enough to resist manipulation. Reputation is the new currency. Like any form of currency, its value will depend on the systems and frameworks you build around it, the trust we place in those systems, and the collective understanding of what it means to be trustworthy.

Reputation and Trust - Rely on Others

Ethical AI on ICP: How Tokenomics Can Drive Responsible Innovation

Lost In Thoughts

Perdut en els pensaments

Perdido en pensamientos

Ajatuksiin eksynyt

Perdido nos pensamentos

אבודים במחשבות

考えにふける

វង្វេងក្នុងគំនិត

생각에 잠겨

ລືມໃນຄວາມຄິດ

Humbur Në Mendime

Лост Ин Тхоугхтс

Загублений у думках

In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge
personal experience
specialist skills
marketing hype
ideology
casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments!

Evidence-Based Management in Business: Will it make a difference?

The rapid advancement of AI demands a shift towards ethical development practices to address privacy, bias, and accessibility concerns while fostering transparency and trust. With the rapid advancement of technology, Artificial intelligence (AI) evolved from a supporting technology to a cornerstone of modern innovation and productivity — accelerating growth across industries. Yet, with the rise of such next-gen systems comes a growing concern: are AI systems being developed ethically? With AI systems inching closer and closer to becoming an integral part of our daily lives, pressing concerns around privacy, bias, and fair access to AI technologies have transformed the notion of ethical AI development from a trend into an urgent imperative. Even though a global governing body for AI ethics does not yet exist, the adoption of ethical AI development practices is crucial for mitigating substantial risks associated with AI. Protecting Data in an AI-Driven World Data privacy is one of the most rapidly emerging ethical challenges in AI development. As AI systems heavily rely on data, often, the process of collecting, storing and use of this data raises significant privacy concerns among consumers. For example, tech giant IBM found itself amidst controversy after several users of its weather app accused the company of tracking and leaking sensitive personal data — without their consent. Stringent safeguards around processing sensitive information not being present can lead to breaches that eventually erode public trust in the technology. Therefore, organizations and developers can avoid legal mishaps by inculcating ethical data collection practices and prioritizing user agreement on transparent data handling, access control, and data deletion policies. These methods can assist preserve user information and build public trust in the AI system's ability to fairly maintain privacy rights. Creating Fair and Inclusive AI Systems Bias in AI models has been a re-occurring issue over the past few years that demands significant attention. When biased or skewed data unknowingly seeps into the training process of AI algorithms, it often leads to the system producing results that can aggravate existing social inequalities. For instance, AI-powered healthcare company Optum was scrutinized by regulators after allegations of its AI app’s algorithm prioritizing white patients over black patients who obtained doctor appointments for more serious health concerns weeks before. This incident highlighted the need for human oversight and diverse representation in datasets being utilized during AI model development. To address such bias, it is essential to include a diverse set of data during the AI training stage and consistently monitor the system for unintended biases. Developers should also follow AI governance frameworks for model development inclusive of policies, best practices and standards to create a balanced and equitable solution. Ensuring that the AI tool/platform is accessible to an individual or organization regardless of their expertise or social status is another important ethical consideration. This can be accomplished by creating open-source projects — where diverse voices can participate to guide development as well as governance of the AI ecosystem. Building an Ethical AI-Driven Future Through ICP A major concern regarding centralized AI platforms, such as OpenAI’s GPT or Google Gemini, is the risk they pose to user data. Most centralized AI platforms often store user data on company servers; diminishing access control over his or her information. However, integrating smart contracts during the development phase of AI models can aid in improved user privacy and security through decentralized storage. The Internet Computer Protocol (ICP) platform enables the development of sophisticated ethical AI systems using decentralized AI technologies. ICP architecture provides developers the ability to run AI training and inference as smart contracts on-chain while powering the computational needs of an AI model and ensuring security with tamper-proof mechanisms and governance protocols. For example, developers can use ICP to realize decentralized storage and provide users with greater control over their information. Open-source projects can also be repurposed to write smart contracts for secure and efficient AI inference. Through its range of user-friendly tools and frameworks, the platform aims to lower the entry barrier and enable a wide range of users to interact with AI technologies. Moreover, inculcating decentralized governance methods during the development phase allows creators to take into account a diverse range of perspectives to train AI models and reduce the possibility of biased outcomes, leading to greater transparency within their AI model ecosystem. On its official page, ICP asserts that the platform will soon enable access to AI hardware including GPUs, to supercharge AI models and their development capabilities via parallel processing. The ICP platform serves as a leading example that despite its complexity, the path to ethical AI development could be shaped through collaborative efforts by developers, users, and policymakers; leading to a future where AI is developed for the greater good of our society. The rapid advancement of AI demands a shift towards ethical development practices to address privacy, bias, and accessibility concerns while fostering transparency and trust. The rapid advancement of AI demands a shift towards ethical development practices to address privacy, bias, and accessibility concerns while fostering transparency and trust. The rapid advancement of AI demands a shift towards ethical development practices to address privacy, bias, and accessibility concerns while fostering transparency and trust. With the rapid advancement of technology, Artificial intelligence (AI) evolved from a supporting technology to a cornerstone of modern innovation and productivity — accelerating growth across industries. Yet, with the rise of such next-gen systems comes a growing concern: are AI systems being developed ethically? With AI systems inching closer and closer to becoming an integral part of our daily lives, pressing concerns around privacy, bias, and fair access to AI technologies have transformed the notion of ethical AI development from a trend into an urgent imperative. With AI systems inching closer and closer to becoming an integral part of our daily lives, pressing concerns around privacy, bias, and fair access to AI technologies have transformed the notion of ethical AI development from a trend into an urgent imperative. Even though a global governing body for AI ethics does not yet exist, the adoption of ethical AI development practices is crucial for mitigating substantial risks associated with AI. Protecting Data in an AI-Driven World Protecting Data in an AI-Driven World Data privacy is one of the most rapidly emerging ethical challenges in AI development. As AI systems heavily rely on data, often, the process of collecting, storing and use of this data raises significant privacy concerns among consumers. For example, tech giant IBM found itself amidst controversy after several users of its weather app accused the company of tracking and leaking sensitive personal data — without their consent. leaking sensitive personal data leaking sensitive personal data Stringent safeguards around processing sensitive information not being present can lead to breaches that eventually erode public trust in the technology. Therefore, organizations and developers can avoid legal mishaps by inculcating ethical data collection practices and prioritizing user agreement on transparent data handling, access control, and data deletion policies. These methods can assist preserve user information and build public trust in the AI system's ability to fairly maintain privacy rights. Creating Fair and Inclusive AI Systems Creating Fair and Inclusive AI Systems Bias in AI models has been a re-occurring issue over the past few years that demands significant attention. When biased or skewed data unknowingly seeps into the training process of AI algorithms, it often leads to the system producing results that can aggravate existing social inequalities. For instance, AI-powered healthcare company Optum was scrutinized by regulators after allegations of its AI app’s algorithm prioritizing white patients over black patients who obtained doctor appointments for more serious health concerns weeks before. This incident highlighted the need for human oversight and diverse representation in datasets being utilized during AI model development. prioritizing white patients prioritizing white patients To address such bias, it is essential to include a diverse set of data during the AI training stage and consistently monitor the system for unintended biases. Developers should also follow AI governance frameworks for model development inclusive of policies, best practices and standards to create a balanced and equitable solution. Ensuring that the AI tool/platform is accessible to an individual or organization regardless of their expertise or social status is another important ethical consideration. This can be accomplished by creating open-source projects — where diverse voices can participate to guide development as well as governance of the AI ecosystem. Building an Ethical AI-Driven Future Through ICP Building an Ethical AI-Driven Future Through ICP A major concern regarding centralized AI platforms, such as OpenAI’s GPT or Google Gemini, is the risk they pose to user data . Most centralized AI platforms often store user data on company servers; diminishing access control over his or her information. However, integrating smart contracts during the development phase of AI models can aid in improved user privacy and security through decentralized storage. risk they pose to user data risk they pose to user data The Internet Computer Protocol (ICP) platform enables the development of sophisticated ethical AI systems using decentralized AI technologies. ICP architecture provides developers the ability to run AI training and inference as smart contracts on-chain while powering the computational needs of an AI model and ensuring security with tamper-proof mechanisms and governance protocols. Internet Computer Protocol (ICP) Internet Computer Protocol (ICP) smart contracts on-chain smart contracts on-chain For example, developers can use ICP to realize decentralized storage and provide users with greater control over their information. Open-source projects can also be repurposed to write smart contracts for secure and efficient AI inference. Through its range of user-friendly tools and frameworks, the platform aims to lower the entry barrier and enable a wide range of users to interact with AI technologies. Moreover, inculcating decentralized governance methods during the development phase allows creators to take into account a diverse range of perspectives to train AI models and reduce the possibility of biased outcomes, leading to greater transparency within their AI model ecosystem. On its official page, ICP asserts that the platform will soon enable access to AI hardware including GPUs, to supercharge AI models and their development capabilities via parallel processing. The ICP platform serves as a leading example that despite its complexity, the path to ethical AI development could be shaped through collaborative efforts by developers, users, and policymakers; leading to a future where AI is developed for the greater good of our society.

From Bias to Fairness: Why Ethical AI Development is Key to Shaping a Just Future

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